model module¶
Scalable model.
This module implements the abstract concept of scalable model which is used by scalable disciplines. A scalable model is built from an input-output learning dataset associated with a function and generalizing its behavior to a new user-defined problem dimension, that is to say new user-defined input and output dimensions.
The concept of scalable model is implemented
through ScalableModel
, an abstract class which is instantiated from:
data provided as a
Dataset
variables sizes provided as a dictionary whose keys are the names of inputs and outputs and values are their new sizes. If a variable is missing, its original size is considered.
Scalable model parameters can also be filled in. Otherwise, the model uses default values.
See also
The ScalableDiagonalModel
class overloads ScalableModel
.
- class gemseo.problems.scalable.data_driven.model.ScalableModel(data, sizes=None, **parameters)[source]
Bases:
object
A scalable model.
- Parameters:
- build_model()[source]
Build model with original sizes for input and output variables.
- Return type:
None
- compute_bounds()[source]
Compute lower and upper bounds of both input and output variables.
- normalize_data()[source]
Normalize the dataset from lower and upper bounds.
- Return type:
None
- scalable_derivatives(input_value=None)[source]
Evaluate the scalable derivatives.
- Parameters:
input_value – The input values. If
None
, use the default inputs.- Returns:
The evaluations of the scalable derivatives.
- Return type:
None
- scalable_function(input_value=None)[source]
Evaluate the scalable function.
- Parameters:
input_value – The input values. If
None
, use the default inputs.- Returns:
The evaluations of the scalable function.
- Return type:
None
- ABBR = 'sm'
- data: IODataset
The learning dataset.